[1]} ([1]}) made use of the first two robust functional principal component scores and presented functional versions of the bagplot and the highest density region plot. With the modified band depth giving the ranks among data, [3]} ([3]}) proposed the functional boxplot, a potent analog to the classical boxplot ([5]} [5]}), for visualizing functional data. For functional data with a dependence structure, [7]} ([7]}) provided an adaptive way of determining the outlier selection factor in the adjusted functional boxplot. [9]} ([9]}) explored the relationship between modified band depth and modified epigraph index ([11]} [11]}) and proposed the outliergram to visualize and detect shape outliers among functional data. [13]} ([13]}) introduced a surface boxplot based on the modified volume depth and developed an interactive surface boxplot tool for the visualization of samples of images. [15]} ([15]}) extended the notion of depths from functional data to curves, presenting the curve boxplot for visualizing ensembles of 2D and 3D curves. [17]} ([17]}) developed a two-stage functional boxplot for multivariate curves, combining directional outlyingness ([19]} [19]}) and a classical functional boxplot in the outlier detection procedure. The two-stage functional boxplot can be applied to functional data both marginally and jointly. Additionally, [21]} ([21]}) introduced the magnitude-shape (MS) plot for visualizing both the magnitude and shape outlyingness of multivariate functional data. [23]} ([23]}) proposed two exploratory tools: the trajectory functional boxplot and the modified simplicial band depth (MSBD) versus wiggliness of directional outlyingness (WO) plot for visualizing trajectory functional data, such as the paths of hurricanes and migrating birds in space. More visualization examples can be found in the recent review of [25]} ([25]}).
